Feature selection for speaker verification using genetic programming

@Article{Loughran2017,
author = "Roisin Loughran and Alexandros Agapitos and
Ahmed Kattan and Anthony Brabazon and Michael O'Neill",
title = "Feature selection for speaker verification using
genetic programming",
journal = "Evolutionary Intelligence",
keywords = "genetic algorithms, genetic programming, Speaker
verification, Feature selection, Unbalanced data",
ISSN = "1864-5917",
DOI = "doi:10.1007/s12065-016-0150-5",
size = "21 pages",
abstract = "We present a study examining feature selection from
high performing models evolved using genetic
programming (GP) on the problem of automatic speaker
verification (ASV). ASV is a highly unbalanced binary
classification problem in which a given speaker must be
verified against everyone else. We evolve
classification models for 10 individual speakers using
a variety of fitness functions and data sampling
techniques and examine the generalisation of each model
on a 1:9 unbalanced set. A significant difference
between train and test performance is found which may
indicate overfitting in the models. Using only the best
generalising models, we examine two methods for
selecting the most important features. We compare the
performance of a number of tuned machine learning
classifiers using the full 275 features and a reduced
set of 20 features from both feature selection methods.
Results show that using only the top 20 features found
in high performing GP programs led to test
classifications that are as good as, or better than,
those obtained using all data in the majority of
experiments undertaken. The classification accuracy
between speakers varies considerably across all
experiments showing that some speakers are easier to
classify than others. This indicates that in such
real-world classification problems, the content and
quality of the original data has a very high influence
on the quality of results obtainable.",
}